Although prior chemical mass balance-based leak detection and water mass balance-based leak detection methods have recognized the importance of process modeling to improve a leak indicator by correcting for otherwise uncharacterized variation, no method has recognized that characterization of leak flow evolution over time is just as important as the system modeling in the extraction of leak-related information. In other words, all sources of variability, whether induced by the system or by the leak itself, must be considered and modeled for detection and estimation purposes. Prior systems limited their attention to models that could be applied at a single instant in time and thus did not make efficient use of all of the data seen to date. In contrast, by incorporating the evolution of the leak over time into the models of the present invention, statistics are created that can efficiently sense leaks that evolve over minutes, hours, or even weeks.
As a result of this failure to incorporate a leak flow model, all prior methods provide just one leak indication statistic. By contrast, the present invention provides a family of statistics, each optimized for the detection of leaks with a specific growth rate. To understand why this is important, it suffices to consider two extreme cases: a slow-growing, small leak and a fast-growing, large leak. Fitting a slow-growing leak profile to the variability associated with a fast-growing, large, leak or vice-versa, results in a poor fit, and, in the extreme case, a reduction of the signal-to-noise ratio to zero. The fact that prior methods were biased towards the detection of leaks with just one growth rate was noted by some practitioners (see Black Liquor Recovery Boiler Leak Detection: Indication of Boiler Water Loss Using a Waterside Chemical Mass Balance Method, by Virginia E. Durham, Paul R. Burgmayer and William E. Pomnitz III), but no method of correcting for this bias was proposed; it was viewed as an innate, qualitative property of the method itself.
In contrast, the present invention can provide significance tests that can effectively detect the presence of both slow-growing and fast-growing leaks. Additionally, in the present invention, these families of leak statistics are combined into one aggregate leak detection signal that provides a single overall signal that will detect leaks of widely varying growth rates in the least possible time.
Boiler water leak detection systems/methods that utilize chemical mass balancing are disclosed in U.S. Pat. Nos. 5,320,967 (Avallone et al.) and 5,569,619 (Thungstrom et al.). In particular, the Avallone et al. patent discloses a boiler leak detection system that determines fluctuations in the measured concentrations of an inert tracer in the boiler water for indicating that a water leak is occurring. However, this method/system is limited by having to detect the tracer when the boiler is at steady state. The Thungstrom et al. patent discloses a boiler leak detection system/method that can operate when the boiler is not a steady state, i.e., where process parameters, such as blowdown rate, feedwater rate and concentration of the boiler water tracer, are changing. However, neither of these two patents analyze the leak data or teach how to assess the statistical significance of the leak data.
In Black Liquor Recovery Boiler Leak Detection: Indication of Boiler Water Loss Using a Waterside Chemical Mass Balance Method, by Virginia E. Durham, Paul R. Burgmayer and William E. Pomnitz III, there is disclsoed a chemical mass balance leak detection system/method that operates in the presence of normal boiler transients and minimizes impact on normal boiler chemistry. The system/method involves measuring the blowdown flow, measuring the amount of chemical delivered to the system with a calibrated chemical feed system, calculating the expected chemical concentration in the blowdown flow (which incorporates chemical feedrate changes and startup conditions, as well as blowdown flow changes and boiler load transients), measuring the blowdown chemical concentration and comparing the actual concentration to the predicted concentration.
U.S. Pat. No. 5,304,800 (Hoots et al.) also discloses another type of chemical mass balance method for detecting leaks in an industrial water process using a temperature-conditioning fluid and a tracer chemical. However, this patent also does not analyze the leak data.
In U.S. Pat. No. 5,363,693 (Nevruz) there is disclosed a recovery boiler leak detection system and method based on water mass balancing which, among other things, uses a much faster sampling rate (e.g., 1/6 sec) than chemical mass balancing (e.g., 15 minutes). In particular, the Nevruz system/method statistically compares moving average values of the boiler drum balance within a short time interval and a longer time interval. A significant difference between the moving averages is attributed to a possible leak. In particular, three moving window pairs (short, medium, and long) are independently used in the model. See A Proven and Patented Method to Detect Small Leaks in Recovery Boilers, by Albert A. Nevruz, TAPPI Proceedings 1995). These three linear filters each represent a difference between a short term average and a corresponding long term average. Because these filters are fixed, they are not readily adaptable to a wide variety of leak, noise and process model situations. For instance, the method has no assumed noise model. Instead, so-called "white" (normally and independently distributed) noise is assumed. Also, the method has no process model to remove artifacts such as steam load effects. Further, there is no assumption of a leak model. This lack of a leak model leads to the situation where leaks of one shape and/or growth rate are preferentially detected over others.
Another boiler leak detection system based on water mass balance is disclsoed in An Expert System for Detecting Leaks in Recovery-Boiler Tubes, by John P. Racine & Henk J. Borsje, June 1992 TAPPI Journal. This system looks for a mismatch between the feedwater entering the drum and the steam and blowdown leaving the boiler. However, the expert system is based on a steady state simulation of the boiler. Furthermore, there is no statistical analysis of any leak data mentioned.
Other boiler leak detection systems include acoustic leak detection as disclsoed in Design and Implementation of a Commercial Acoustic Leak-Detection System for Black Liquor Recovery Boilers, by Gregory D. Buckner & Stephen J. Paradis, July 1990 TAPPI Journal. This system basically utilizes acoustic transducers for detecting noise levels that exceed basic boiler noise levels for a certain amount of time as being indicative of a boiler leak.
Thus, there remains a need for a boiler leak detection/estimation system and method that provides a family of statistics based upon actual leak flows, each optimized for the detection of leaks with a specific growth rate. Additionally, there remains a need for a boiler leak detection estimation system and method that combines this family of detection signals into a signal easily interpreted by boiler operators.